Document resource
Introduction Breast cancer poses substantial public health and economic burdens worldwide, particularly in Hong Kong, where incidence has risen fivefold in the last 30 years. While mammography (MMG) screening facilitates prognosis, early cancer detection has the potential to better use limited healthcare resources within a hospital system.Methods A retrospective cohort study was conducted using data from the Hong Kong Breast Cancer Registry, which included 15 144 cases (13 502 self-detected and 1642 MMG-detected breast cancers) diagnosed between 2006 and 2018. A subgroup of 6359 cases (diagnosed 2006–2011) underwent Kaplan-Meier survival analysis to compare 10-year overall survival between detection methods. Population-based Markov models were developed to simulate 100 000 average-risk women aged 40 and to assess clinical outcomes, treatment costs and cost-minimisation analysis of MMG screening from the healthcare provider’s perspective.Results MMG-detected breast cancers presented significantly earlier at diagnosis, with higher proportions of stage 0 (33.2% vs 5.4%, p<0.001) and stage I (48.3% vs 31.5%, p<0.001) cancers, smaller tumour sizes (mean: 1.3 cm vs 2.3 cm, p<0.001) and less aggressive biological subtypes compared with self-detected cases. 10-year overall survival was notably higher in MMG-detected patients (95.7% vs 88.4%, log-rank p<0.001). Treatment costs per patient were 28.4% lower for MMG-detected vs self-detected cancers, driven primarily by stage downshifting. The Markov model demonstrated that MMG screening starting at age 40 in 100 000 women could prevent 27 932 life-years lost and save $98.8 million in treatment costs compared with self-detection. Benefits decreased if screening commenced at a later age.Conclusions MMG screening in Hong Kong can significantly reduce mortality and healthcare expenditures due to earlier cancer detection. Policymakers and healthcare providers should consider expanding MMG screening programmes, particularly targeting women aged 40 and above, to optimise clinical outcomes and resource utilisation.